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Optimized motion simplification for crowd animation

机译：优化了人群动画的动作简化

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Simulating a huge number of articulate figures in a real-time application is one of the challenging research topics in character animation. Several researchers have tried to improve the performance of animation using the image-based technique such as 'impostor.' This method improved the speed of the animation; however, the accuracy, memory, and interactivity problems related to motion remain to be resolved. In this regard, a 'motion simplification' framework for an articulate figure is proposed, which not only improves the speed of the animation but also conserves the features of the original motion. First, the motion sequence is analyzed using mean shift clustering for the purpose of extracting key postures and automatically generating the priority of joint reductions. These motion analysis results are directly utilized as the input of the proposed posture optimization. The least square method is applied in order to minimize the error between the original and the simplified posture. Finally, how to apply the proposed simplified motion in a real-time application without decreasing the visual quality of the scene is shown. The experimental result shows that the proposed motion simplification can be successfully applied in a realtime animation system.
机译：在实时应用程序中模拟大量清晰表达的人物是角色动画中具有挑战性的研究主题之一。一些研究人员已尝试使用基于图像的技术（例如“ impostor”）来改善动画的性能。这种方法提高了动画的速度。但是，与运动有关的准确性，记忆力和交互性问题仍有待解决。在这方面，提出了一种针对关节人物的“运动简化”框架，该框架不仅提高了动画的速度，而且保留了原始运动的特征。首先，使用运动均值聚类分析运动序列，以提取关键姿势并自动生成关节复位的优先级。这些运动分析结果直接用作提议的姿势优化的输入。为了最小化原始姿势和简化姿势之间的误差，应用了最小二乘法。最后，显示了如何在不降低场景视觉质量的情况下将建议的简化运动应用于实时应用。实验结果表明，所提出的运动简化方法可以成功地应用于实时动画系统中。

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